Learn TensorFlow 2021 – Best TensorFlow tutorials & Best TensorFlow courses & Best TensorFlow books

Best TensorFlow Courses 2021


Best TensorFlow Books 2021

Best TensorFlow tutorials 2021

Complete Guide to TensorFlow for Deep Learning with Python

This course will walk you through using Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow framework in an easy to understand way. Other courses and tutorials tend to stay away from the flow of pure tension and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning. This course covers a variety of topics, including

Neural Network Basics
TensorFlow Basics
Artificial Neural Networks
Densely Connected Networks
Convolutional Neural Networks
Recurrent Neural Networks
Reinforcement Learning
OpenAI Gym

This is the best TensorFlow tutorial in 2021.

A Complete Guide on TensorFlow 2.0 using Keras API

The course is structured to cover all topics of neural network modeling and training to put it into production. In the first part of the course, you will learn about the technology stack that we will be using throughout the course (section 1) and the basics and syntax of the TensorFlow 2.0 library (section 2). In the second part of the course, we will delve into the exciting world of deep learning. During this part of the course, you will implement several types of neural networks (fully connected neural network (section 3), convolutional neural network (section 4), recurrent neural network (section 5)). By the end of this part, section 6, you will learn and build their own transfer learning application that will achieve cutting edge results (SOTA) on the Dogs vs Cats dataset.

After successfully completing part 2 of the course and finally learning how to implement neural networks, in part 3 of the course you will learn how to create your own stock trading bot using Reinforcement Learning, in particular the Deep-Q network. . about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and how to create your own data pipelines for production. In section 8 we will check if the dataset has anomalies using the TensorFlow Data Validation library and after learning how to check a dataset for anomalies in section 9 we will create our own pipeline data preprocessing using the TensorFlow Transform library.

In Section 10 of the course, you will learn and build your own Fashion API using Flask Python library and a pre-trained model. Throughout this section you will have a better idea of ​​how to send a request to a model on the internet. However, at this point, the architecture around the model is not scalable for millions of requests. Enter section 11. In this section of the course, you will learn how to improve the solution from the previous section using the TensorFlow Serving library. In a very easy way, you will learn and create your own image classification API which can support millions of requests per day! Nowadays, it is more and more popular to have a Deep Learning model in an Android or iOS app, but neural networks require a lot of power and resources! This is where the TensorFlow Lite library comes in. In Section 12 of the course, you will learn how to optimize and convert any neural network to make it suitable for a mobile device.

You will learn:
How to use Tensorflow 2.0 in Data Science
Important differences between Tensorflow 1.x and Tensorflow 2.0
How to implement artificial neural networks in Tensorflow 2.0
How to implement convolutional neural networks in Tensorflow 2.0
How to implement recurrent neural networks in Tensorflow 2.0
How to Create Your Own Transfer Learning App in Tensorflow 2.0
How to Create a Stock Trading Bot Using Reinforcement Learning (Deep-Q Network)
How to create a machine learning algorithms in Tensorflow 2.0
How to perform data validation and preprocessing of datasets using TensorFlow data validation and TensorFlow transformation.
Production of a TensorFlow 2.0 model
How to create a Fashion API with Flask and TensorFlow 2.0
How to serve a TensorFlow model with the RESTful API

Tensorflow 2.0: Deep Learning and Artificial Intelligence

Tensorflow is Google’s library for deep learning and artificial intelligence. Deep Learning has recently been responsible for some amazing achievements, such as:

Generate beautiful photorealistic images of people and objects that never existed (GAN)
Defeat World Champions in Go Strategy Game and Complex Video Games like CS: GO and Dota 2 (Deep Reinforcement Learning)
Autonomous cars (computer vision)
Speech recognition (e.g. Siri) and machine translation (natural language processing)
Even create videos of people doing and saying things they’ve never done (DeepFakes – a potentially harmful deep learning app)

Along the way, you’ll learn about all of the major deep learning architectures, such as deep neural networks, convolutional neural networks (image processing), and recurrent neural networks (sequence data).
Current projects include:
Natural language processing (NLP)
Recommendation systems
Learning transfer for computer vision
Generative Adversary Networks (GANs)
Deep Reinforcement Learning Stock Trading Bot

You will learn:
Artificial Neural Networks (ANN) / Deep Neural Networks (DNN)
Predict stock returns
Time series forecasts
Computer vision
How to Create a Deep Reinforcement Learning Stock Trading Bot
GANs (Generative Adversarial Networks)
Recommendation systems
Image recognition
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
Use Tensorflow’s distribution strategies to parallelize learning
Low-level tensorflow, gradient ribbon and how to create your own custom models
Natural Language Processing (NLP) with Deep Learning
Demonstrate Moore’s Law Using Code
Transfer learning to create state-of-the-art image classifiers

Complete Tensorflow 2 and Keras Deep Learning Bootcamp

This course will guide you in using Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy-to-understand guide to the complexities of Google’s TensorFlow 2 framework in an easy-to-understand manner. We will focus on understanding the latest updates to TensorFlow and using the Keras API (the official API of TensorFlow 2.0) to quickly and easily build models. In this course, we will create models to predict future prices, classify medical images, predict future sales data, artificially generate complete new text, and much more! This course is designed to balance theory and practical implementation, with comprehensive jupyter notebook code guides and easy-to-navigate slides and notes. This course covers a variety of topics:

Intensive NumPy course
Introductory course in Pandas data analysis
Introductory course in data visualization
Learning Neural Networks Basics
TensorFlow Basics
Basics of Keras syntax
Artificial neural networks
Densely connected networks
Convolutional neural networks
Recurrent neural networks
Automatic encoders
GAN – Generative Conflict Networks
Deployment of TensorFlow in production
Learn how to use TensorFlow 2.0 for Deep Learning
Leverage the Keras API to quickly build models that run on Tensorflow 2
Perform image classification with convolutional neural networks
Using Deep Learning for Medical Imaging
Predicting time series data with recurrent neural networks
Use generative conflicting networks (GANs) to generate images
Use deep learning for style transfer
Generate text with RNNs and natural language processing
Serve Tensorflow models via an API
Use GPUs for accelerated deep learning

Best TensorFlow books 2021

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...
  • Géron, Aurélien (Author)
  • English (Publication Language)
  • 856 Pages - 10/15/2019 (Publication Date) - O'Reilly Media (Publisher)

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron. With a number of recent advancements, deep learning has energized the entire field of machine learning. Now, even programmers who know almost nothing about this technology can use simple and effective tools to implement programs capable of learning from data.Using real life examples, minimal theory, and two production-ready Python frameworks, Scikit-Learn and TensorFlow, author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems in this practical book. He will learn a variety of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help him apply what he has learned, all he needs is a programming experience to get started.

Explore the machine learning landscape, especially neural networks
Use Scikit-Learn to follow a sample machine learning project from start to finish
Explore multiple training models, including support vector machines, decision trees, random forests, and ensemble methods
Use the TensorFlow library to build and train neural networks
Immerse yourself in neural network architectures, including convolutional networks, recurring networks, and deep reinforcement learning
Learn techniques for training and scaling deep neural networks.

This is one of the best TensorFlow book in 2021.

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with...
  • Gulli, Antonio (Author)
  • English (Publication Language)
  • 646 Pages - 12/27/2019 (Publication Date) - Packt Publishing (Publisher)

by Antonio Gulli, Amita Kapoor, Sujit Pal teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You will learn how to write deep learning applications on the most powerful, popular, and scalable machine learning stack available. TensorFlow is the preferred machine learning library for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 offers full Keras integration, making advanced machine learning easier and more convenient than ever.

This book also introduces Neural Networks with TensorFlow, walks through the main applications (Regression, ConvNets (CNN), GAN, RNN, NLP), covers two examples of functional applications, then delves into TF in production, mobile TF, and using TensorFlow with AutoML.
What you are going to learn

Build machine learning and deep learning systems with TensorFlow 2 and the Keras API
Use regression analysis, the most popular approach to machine learning
Understand ConvNets (convolutional neural networks) and how they are essential to deep learning systems such as image classifiers.
Use GAN (Generative Antagonistic Networks) to create new data that matches existing patterns
Discover RNNs (Recurrent Neural Networks) that can intelligently process input sequences, using one part of a sequence to correctly interpret another.
Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response.
Train your models in the cloud and put TF to work in real environments
See how Google tools can automate simple ML workflows without the need for complex modeling

This book is intended for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice you need to use Keras, TensorFlow 2, and AutoML to create machine learning systems. Some knowledge of machine learning is expected.

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition”]

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised...
  • Atienza, Rowel (Author)
  • English (Publication Language)
  • 512 Pages - 02/28/2020 (Publication Date) - Packt Publishing (Publisher)

Advanced Deep Learning with TensorFlow 2 and Keras by Rowel Atienza is a fully updated edition of the Guide to Successful Advanced Deep Learning Techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), allowing you to create your own own cut. cutting edge artificial intelligence projects.Using Keras as an open source deep learning library, the book features hands-on projects that show you how to create more efficient AI with the latest techniques.

Starting with an overview of multilayer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN), the book introduces more cutting-edge techniques as you explore neural network architecture programs, including ResNet and DenseNet, and how to create automatic encoders. Next, you will learn about GANs and how they can unlock new levels of AI performance. Next, you will discover how a Variational Autoencoder (VAE) is implemented and how GANs and VAEs have the generating power to synthesize data that can be extremely attractive to humans. You will also learn how to implement DRLs such as Deep Q-Learning and Policy Gradient Methods, which are essential for many modern results in AI.

Learning TensorFlow: A Guide to Building Deep Learning Systems

Learning TensorFlow: A Guide to Building Deep Learning Systems
  • Hope, Tom (Author)
  • English (Publication Language)
  • 242 Pages - 09/12/2017 (Publication Date) - O'Reilly Media (Publisher)

Learning TensorFlow by Tom Hope, Yehezkel S. Resheff and Itay Lieder gives a hands-on approach to TensorFlow fundamentals. Inspired by the human brain, deep neural networks made up of huge amounts of data can solve complex tasks with unprecedented precision. This book provides an end-to-end guide to TensorFlow’s top open source software library that helps you build and train computer perspectives, automated natural language processing (NLP), neural networks for recognition, vocal and general predictive analysis.

Authors Tom Hope, Ezekiel Risheff, and Itte have proposed a hands-on approach to the basics of tensorflow to a wide range of technological audiences, from scientists and data engineers to students and researchers. Before delving further into topics such as neural network architecture, tensorboard visualization, tensorflow abstraction libraries, and multithreaded input pipelines, you will begin to study some basic examples in tensorflow. By the end of this book, you will know how to create and set up a production-ready deep learning system in TensorFlow.

Machine Learning with TensorFlow

Machine Learning with TensorFlow
  • Shukla, Nishant (Author)
  • English (Publication Language)
  • 272 Pages - 02/12/2018 (Publication Date) - Manning Publications (Publisher)

Machine Learning with TensorFlow by Nishant Shukla will give you a solid foundation in machine-learning concepts with hands-on experience coding TensorFlow with Python. This TensorFlow book will teach you how to use TensorFlow for machine-learning and building deep-learning applications. Machine learning with TensorFlow gives readers a solid foundation in machine learning concepts as well as coding experience with TensorFlow with Python Hands TensorFlow, Google’s library for larger scale machine learning Makes.

Machine learning with TensorFlow gives readers a solid foundation on machine learning concepts as well as coding experience with TensorFlow with Python hands You will learn the basics by working with classic predictions, classification and clustering algorithms. Next, you’ll move on to the chapters on finance: explore deep learning concepts such as auto-encoder, repetitive neural networks, and reinforcement learning.

Deep Learning with Python

Deep learning with Python introduces the realm of deep learning using the Python language and the powerful Keras library. Written by François Chollet, creator of Keras and Google’s artificial intelligence researcher, this book reinforces understanding of it with intuitive explanations and practical examples. You will explore challenging concepts and practices with applications of computer vision, natural language processing, and generative modeling. When you are done, you will have the knowledge and practical skills to apply deep learning in your own projects.

You will learn:
Learn deeply from first principles
Set up your own deep learning environment
Image classification models
Deep learning for text and sequences
Neural style transfer, text generation, and image generation

Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

Computer vision solutions are becoming more common and are making their way in fields such as healthcare, automotive, social media, and robotics. This book will help you explore TensorFlow 2, the latest version of Google’s open source framework for machine learning. You will understand how to take advantage of the use of convolutional neural networks (CNN) for visual tasks.

Hands-on computer vision with TensorFlow 2 by Benjamin Planche and Eliot Andres starts with the fundamentals of computer vision and deep learning, and teaches you how to build a neural network from scratch. You’ll learn about the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface, and move on to effective CNN creation, training, and deployment. Complete with real-world code examples, the book shows how to classify images with modern solutions such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U -Net. It will also create Generative Conflict Networks (GAN) and Variational Automatic Encoders (VAE) to create and edit images, and LSTM to analyze video. In the process, he will gain advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.

Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

In this how-to guide, Hannes Hapke and Catherine Nelson walk you through the steps to automate a machine learning pipeline using the TensorFlow ecosystem. You’ll learn techniques and tools that will reduce deployment time from days to minutes, so you can focus on developing new models rather than maintaining legacy systems.

Data scientists, machine learning engineers and DevOps engineers will discover how to go beyond model development to successfully produce their data science projects, while managers will better understand the role they play in accelerating these Projects.

Understand the stages that make up a machine learning pipeline
Create your pipeline with TensorFlow Extended components
Organize your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines
Work with data using TensorFlow data validation and TensorFlow transformation
Analyze a Model in Detail with TensorFlow Model Analysis
Examine the fairness and bias in your model’s performance
Deploy models with TensorFlow Serving or convert to TensorFlow Lite for mobile devices
Understand machine learning techniques that preserve privacy


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